formulas <- make.formulas(mi_2)
method <- make.method(mi_2)
mi_multiple_imp <- parlmice(mi_2,
method = method,
formulas = formulas,
m = 1,
n.core = 3,
cluster.seed = 12,
n.imp.core = 2,
cl.type = "FORK")
plot(mi_multiple_imp)
mi_2.5 <- complete(mi_multiple_imp, action = "long", include = TRUE)
mi_3 <- complete(mi_multiple_imp, action = 1, include = FALSE)
save.image("mi_mult_imp.RData")
plot(mi_multiple_imp)
log_reg_mod <- logistic_reg(
mode = "classification",
engine = "glmnet",
penalty = 0.001,
mixture = 0
)
log_reg_rec <- recipe(Complication ~ Total_NSIDS., data = mi_2) %>%
step_impute_knn(all_numeric_predictors())
log_reg_wkflow <- workflow() %>%
add_recipe(log_reg_rec) %>%
add_model(log_reg_mod)
log_reg_fit <- fit(log_reg_wkflow, data = mi_2)
tidy(log_reg_fit)
# Augmented model
model_aug <- augment(log_reg_fit, truth = Complication,
new_data = mi_2)
# roc score
auc_score <- roc_auc(data = model_aug, truth = Complication,
estimate = .pred_1, event_level = "second")
# plot of roc curve
autoplot(roc_curve(data = model_aug, truth = Complication,
estimate = .pred_1, event_level = "second"))
save.image("SIBS_log_reg.Rdata")